E-commerce has developed rapidly during the COVID-19 pandemic resulting in an increased demand for last-mile deliveries [1
]. In 2021, retail e-commerce sales were 5.2 trillion US dollars worldwide in comparison to 3.4 trillion U.S. dollars in 2019 [4
]. For example, in 2020 alone, e-commerce sales resulted in 125 billion parcel and courier shipments [5
]. The e-commerce market has become very competitive, and to be attractive to customers, companies offer services related to shortening delivery times (e.g., same-day delivery or a less than 24 h guarantee for delivery) [6
]. The relationship between cost, speed, and carbon emission values in e-commerce is recently under investigation by researchers [7
]. Speed-oriented distribution services in e-commerce result in lower consolidation levels, and therefore negative externalities [8
], such as congestion and air pollution. In e-commerce, outbound logistics (e-commerce-related deliveries) are responsible for almost half of the direct and indirect greenhouse gas emissions in the value chain [9
In the European Union, more than 75% of the population lives in cities; thus, most e-commerce deliveries take place in urban areas. Urban deliveries generate more than 25% of CO2
emissions in a city [10
]. Due to the rapid development of e-commerce, a considerable share of urban deliveries is made directly to individual customers, so the use of traditional trucks is less effective [11
]. Urban freight transport generates more pollution per km traveled than long-distance transport, due to higher fuel consumption, as there are more frequent stops (stop-and-go movements) and a higher share of idle times per trip [12
]. The lower fuel efficiency of the last-mile operations also results from a spatial distribution of recipients. Urban e-commerce deliveries are more frequent and smaller with different time windows and non-optimal rescheduling due to the absence of recipients [14
According to the European Commission [10
], approximately 20.3% of the world′s emissions are from the transportation sector. Previous empirical studies have shown that more than 20% of CO2
emissions related to transport come from urban freight vehicles [15
Many researchers have focused on the decarbonization of urban freight through its electrification [16
], and the use of alternative modes of transportation such as cargo bikes/tricycles [19
], and drones [14
]. However, in business practices, most urban deliveries are still made by trucks and vans.
Therefore, there is a strong need for an actionable approach to measuring the CO2
emissions created by trucks and vans [21
]. In this paper, we derive from theory and practice (focused survey) the parameters that are relevant for fuel consumption in urban areas and are related to the characteristics of the fleet. To our knowledge, existing theoretical studies do not provide a direct answer about specific methods of CO2
emissions calculated within supply chains in terms of various truck parameters. Therefore, our research contributes to this knowledge gap. The main objective of the study is to propose a practice-oriented method for calculating the total CO2
emissions in a supply chain with parameters relevant to e-commerce deliveries with a heterogeneous fleet. We focus on fuel consumption (energy efficiency) and its relation to the structure of a fleet. In this study, we aim to answer two research questions:
RQ1: What factors influence the CO2 emissions of vehicles in e-commerce distribution?
RQ2: How can direct CO2 emissions be calculated in e-commerce deliveries in an actionable way taking into account the heterogeneous fleet structure?
We search for the answer to RQ1 through the literature review and empirical studies (focused survey) among freight managers, who specialized in e-commerce deliveries.
In order to answer RQ2, a calculation formula is proposed that is useful for determining the direct CO2 emissions for a heterogeneous fleet that delivers goods in e-commerce. We performed numerical examples to test the proposed formula using real-life data and DEKRA emission′s factors.
The novelty of this research results from the proposal of a new actionable approach to calculate CO2 emissions from heterogeneous fleets in e-commerce deliveries. Through a literature review, a survey among transport managers, and a case study on e-commerce delivery, we found that the heterogeneous fleet structure impacts direct emissions from urban deliveries. We have taken into account the parameters of the heterogeneous fleet structure, such as gross weight, mass, fuel type, engine size, fuel consumption (liters/100 km), and the age of a vehicle.
The remainder of the paper is organized as follows: The research approach and methods are described in Section 2
. The results of the focused survey and numerical experiments are presented in Section 3
. The discussion on the application of the proposed approach is provided in Section 4
. The final conclusions are stated in Section 5
3. Materials and Methods
3.1. Research Approach
Our research methodology combines a literature review, a survey among transport managers, and a case study on e-commerce delivery. The research approach is presented in Figure 1
3.2. Classification of Factors
Based on the literature review (see Section 2.3
), we classify the factors in Table 1
into three categories and present them in Figure 2
. The literature review was not conclusive on the characteristics of the fleet. For that reason, we determined the characteristics during a brainstorming session with five fleet managers (with a minimum of 5 years of professional experience in distribution for e-commerce purposes) from two major logistics companies in Poland, which are responsible for fleet management in urban deliveries for e-commerce purposes. During the brainstorming session, they identified the parameters that are relevant to describe the fleet, as follows:
The information obtained was investigated in the focus-group survey.
To measure the carbon footprint in an actionable way, it is necessary to obtain reliable data on the characteristics of the fleet. A focused survey has been conducted among transport managers (TM) responsible for fleet management for e-commerce distribution. The scope of the survey covered only trucks that were dedicated to daily domestic deliveries (Polish) of e-commerce parcels.
Data were collected regarding the daily distance travelled and the fleet characteristics. The experts had to provide a complete set of information on the truck type, GVM, engine type, vehicle age, and average fuel consumption (liters per 100 km).
The survey was carried out among carriers with various subcontractors with different means of transport (a heterogeneous fleet). The scope of the survey included experts (transport managers) from 2 distribution companies and their 13 subcontracted carriers.
The experts included the Director of the Transportation Department, Director of Logistics and Technologies, Logistics Director, Supply Chain Development Director, and Managing Director. The responsibilities of the individual experts are presented in Table 2
The fleet under consideration included trucks and vans used for urban distribution in e-commerce. In total, 22 types of vehicles were analyzed. In consequence, 56 datasets were collected across nine different vehicle brands. Additionally, data on fuel consumption were recorded for each truck. The data were stored in a database supported by the MS Excel tool. All records containing the manufacturer name, brand, and truck type name were anonymized. The data collected as a result of the surveys contained detailed parameters related to the average monthly mileage of the vehicle, its age, and the average fuel consumption. The surveys also collected data on the brand and model of the vehicle, as well as its weight (GVM). However, due to the purpose of processing the collected data, this information was anonymized. Data were collected in a tabular form, which made it easier to verify the interdependencies between the various parameters of the vehicles in the next part of this study.
The data were also used for the assessment of the total fuel consumption and direct CO2 emissions from distribution activities.
Studies on CO2
emissions in a supply chain focus mainly on manufacturing [71
] and sourcing activities [73
]. Empirical studies on emissions related to distribution in commerce deliveries are very limited [18
Our previous studies [74
] have shown that the reported CO2
emissions values depend on the method used for the calculation. Methods for measuring the carbon footprint do not fully reflect real-life needs. Theoretical studies focus mainly on the LCA-based approach, which is very complex and requires a significant amount of internal and external data. Most studies take into account the homogeneous fleet and focus on fuel- and road-related factors. In this study, we focused on empirical research in order to identify factors that are relevant for the calculation of direct CO2
emissions in e-commerce deliveries and are the responsibility of fleet managers. We focus on a heterogeneous fleet structure, as it is common in real life and is not sufficiently investigated in the literature. Real-life e-commerce deliveries are performed with a heterogeneous fleet, as transport activities are often outsourced to different partners in a supply chain [61
]. In our approach, we determined factors based on previous studies on fleet characteristics and their relationship to CO2
], which identified factors that allow for the parametrization of a heterogeneous fleet, such as vehicle type, vehicle size/weight, technology type (Euro standard), and fuel type. Through an exploratory analysis, we additionally identified the age of a vehicle as a factor that influences fuel consumption and related CO2
emissions. In this study, we determined the answers to two research questions. As a result of the literature review and a survey with transportation management, we obtained additional information on fuel consumption and its link to the characteristics of the fleet. Through numerical experiments, we identified that the age of a vehicle results in increased fuel consumption of 1.31% year-to-year for vans and 1.01% year-to-year for 18-t trucks. For that reason, we proposed a novel formula to calculate the direct CO2
emissions from heterogeneous fleets in e-commerce deliveries, which takes into consideration the age-related fuel consumption factor and the structure of a fleet.
The results have theoretical and practical implications. The theoretical contribution is a novel actionable approach for the calculation of the direct CO2
emissions from a heterogeneous fleet in e-commerce deliveries. Previous studies highlight the role of logistics service providers in the development of a sustainable supply chain and the need for further investigation in this area [75
]. Most existing studies refer to the fuel efficiency of vehicles, e.g., [77
], in relation to different environmental conditions. In this article, we conducted an exploratory analysis (a focused survey) on the structure (GVM and vehicle age) as a factor influencing emissions levels, which extends the existing body of literature.
This work has managerial implications, as it highlights the role of the fleet structure in the calculation of CO2 emissions. The structure of the fleet is the responsibility of transport managers. They can benefit from the findings of this paper in short- and long-term managerial decision making. First, the proposed formula is actionable as it does not require conducting complex and time-consuming data collection. We based our CO2 emissions calculations on information available to fleet managers on a daily basis or can be easily extracted from the internal database of a company, such as the number of vehicles, ages of vehicles, GVM, distance travelled, and fuel consumption. The fleet manager can use the proposed approach to manage direct fleet emissions in the urban distribution network for e-commerce purposes. They can use the simplified formula for the simulation of the preferred fleet structure (how many trucks, how many vans, and the ages of vehicles) for a particular customer from the e-commerce sector or for a particular period of time, in order to meet the goal of CO2 reduction. Second, the decision can be used in the long term to support fleet renewal decisions in terms of the potential reduction of CO2 emissions.
The purpose of this study is to provide a novel and actionable approach to the calculation of direct CO2 emissions from a heterogeneous fleet for the purpose of e-commerce deliveries in urban areas. The provided formula allows for the simplified calculation of CO2 without the need for performing resource-intensive LCA calculations. We investigated the answer to RQ1 through the literature review and empirical studies (focused survey) among freight managers, who specialized in e-commerce deliveries. We categorized the factors that influence direct CO2 emissions in urban deliveries for urban purposes into three categories: (1) Cargo, road, and traffic characteristics; (2) fuel characteristics; and (3) fleet characteristics. After the brainstorming session with fleet managers, we focused on the GVM, age of a vehicle, number of vehicles, and fuel consumption. We accounted for the distance traveled in the linehauls and last-mile deliveries. Using the data from the survey, we identified the increase in fuel consumption with age. Due to the research conducted among carriers, it was possible to determine the average annual increase in fuel consumption based on the age of a vehicle and vehicle GVM.
To answer RQ2, a calculation formula was proposed that is useful to determine direct CO2 emissions for a heterogeneous fleet that delivers goods in e-commerce. We performed numerical examples to test the proposed formula using real-life data and DEKRA emission factors.
The novelty of this research results from the new and easy-to-apply approach to calculate CO2
emissions from heterogeneous fleets in e-commerce deliveries, which takes into account the GVM, type of vehicle, and vehicle age. Therefore, the proposed method can be used to estimate the levels of greenhouse gas in the case of online sales and related deliveries (distribution). The fleet used for e-commerce deliveries is often older than that used for long-haul transportation [78
]. Furthermore, the COVID-19 pandemic and current economic crisis can also result in a slower renewal of vehicle fleets, with older, more energy-consuming and polluting vehicles being used for longer periods (before replacement by newer models) [79
]. Thus, this paper contributes to theory and practice, as it allows one to parametrize the influence of vehicle age on the increase in fuel consumption and related CO2
The limitations of this study result from the fact that, in data collection, we rely on expert knowledge (transport managers). Therefore, there may be some differences depending on the fleet structure used and the drivers’ qualifications (e.g., eco-driving training). In order to mitigate this limitation, we triangulated the empirical data with manufacturers′ data regarding the fuel consumption of new trucks/vans. Fuel consumption data sourced from truck/van manufacturers were collected and sorted by GVM class. Actual data sourced from various dealers and brands are presented in Appendix A
. The proposed approach was also implemented in business practice and was positively assessed as actionable and feasible by the transport managers who participated in the case study.
Future research will be conducted to verify mutual interconnections between heterogeneous fleet characteristics such as GVM, engine capacity, mileage, and fuel consumption to identify other relevant factors that will extend the proposed calculation formula.